Identify orphaned data in Databricks with Secoda. Learn more about how you can automate workflows to turn hours into seconds. Do more with less and scale without the chaos.
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Integration with Databricks allows for the identification of data that no longer has an assigned team or user, which is important in data cleanup practices. This helps prevent data integrity issues, optimize storage use, enhance system performance, and ensure accurate data analysis and reporting processes. By tagging such orphaned data for review, organizations can maintain a streamlined and efficient data environment.
Integration with Databricks enables automated workflows consisting of triggers and actions. Triggers activate the workflow based on predefined schedules, allowing for structured and timely execution of subsequent actions. Actions encompass various operations, such as filtering and updating metadata. By stacking actions, customized and detailed workflows can be created to meet specific team requirements. With Secoda, it is possible to perform bulk updates to metadata in Databricks. A specific use case is to identify data in Databricks that lacks an assigned team or user and tag it for review.
Secoda, a data management platform, consolidates your company's data catalog, lineage, documentation, and monitoring. By integrating Databricks with Secoda, data teams can efficiently scale their data enablement practices. This integration simplifies the process of maintaining trust scorecards, facilitating the expansion of data teams' capabilities.
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